Small ad hoc versus large general training populations for genomewide selection in maize biparental crosses.

KEY MESSAGE: For genomewide selection in each biparental population, it is better to use a smaller ad hoc training population than a single, large training population. In genomewide selection, different types of training populations can be used for a biparental population made from homozygous parents (A and B). Our objective was to determine whether the response to selection (R) and predictive ability (rMP) in an A/B population are higher with a large training population that is used for all biparental crosses, or with a smaller ad hoc training population highly related to the A/B population. We studied 969 biparental maize (Zea mays L.) populations phenotyped at four to 12 environments. Parent-offspring marker imputation was done for 2911 single nucleotide polymorphism loci. For 27 A/B populations, training populations were constructed by pooling: (1) all prior populations with A as one parent (A/*, where * is a related inbred) and with B as one parent (*/B) [general combining ability (GCA) model]; (2) A/* or */B crosses only; (3) all */* crosses (same background model, SB); and (4) all */*, A/*, and */B crosses (SB + GCA model). The SB model training population was 450-6000% as large as the GCA model training populations, but the mean coefficient of coancestry between the training population and A/B population was lower for the SB model (0.44) than for the GCA model (0.71). The GCA model had the highest R and rMP for all traits. For yield, R was 0.22 Mg ha-1 with the GCA model and 0.15 Mg ha-1 with the SB model. We concluded that it is best to use an ad hoc training population for each A/B population.

Ascertainment bias from imputation methods evaluation in wheat.

BACKGROUND: Whole-genome genotyping techniques like Genotyping-by-sequencing (GBS) are being used for genetic studies such as Genome-Wide Association (GWAS) and Genomewide Selection (GS), where different strategies for imputation have been developed. Nevertheless, imputation error may lead to poor performance (i.e. smaller power or higher false positive rate) when complete data is not required as it is for GWAS, and each marker is taken at a time. The aim of this study was to compare the performance of GWAS analysis for Quantitative Trait Loci (QTL) of major and minor effect using different imputation methods when no reference panel is available in a wheat GBS panel. RESULTS: In this study, we compared the power and false positive rate of dissecting quantitative traits for imputed and not-imputed marker score matrices in: (1) a complete molecular marker barley panel array, and (2) a GBS wheat panel with missing data. We found that there is an ascertainment bias in imputation method comparisons. Simulating over a complete matrix and creating missing data at random proved that imputation methods have a poorer performance. Furthermore, we found that when QTL were simulated with imputed data, the imputation methods performed better than the not-imputed ones. On the other hand, when QTL were simulated with not-imputed data, the not-imputed method and one of the imputation methods performed better for dissecting quantitative traits. Moreover, larger differences between imputation methods were detected for QTL of major effect than QTL of minor effect. We also compared the different marker score matrices for GWAS analysis in a real wheat phenotype dataset, and we found minimal differences indicating that imputation did not improve the GWAS performance when a reference panel was not available. CONCLUSIONS: Poorer performance was found in GWAS analysis when an imputed marker score matrix was used, no reference panel is available, in a wheat GBS panel.

Predicted Genetic Gains from Targeted Recombination in Elite Biparental Maize Populations.

Targeted recombination is the ability to induce or select for specific recombination points on chromosomes. A first study with the intermated B73 × Mo17 maize ( L.) population showed that targeted recombination doubles the predicted gains for yield and other agronomic traits. Our objective was to assess the predicted gains from targeted recombination for quantitative traits in multiple, elite maize populations. A total of 969 biparental maize populations were phenotyped at four to 12 environments in the United States from 2000 to 2008. Positions of one and two targeted recombinations per chromosome were determined from genomewide marker effects for 2911 single nucleotide polymorphism (SNP) loci. Relative efficiency (RE) was calculated as the predicted response to targeted recombination divided by the predicted response to nontargeted recombination. On average, targeted recombination doubled the predicted genetic gains for yield, moisture, and test weight. For each trait, RE ranged from around 60 to 400% among the populations, and targeted recombination did not increase gains in around 4% of the populations. The RE tended to decrease as the similarity between the parents increased. Having targeted recombination on three chromosomes (for yield and test weight) to seven chromosomes (for moisture) led to the same or greater predicted gain than nontargeted recombination. Marker intervals for targeted recombination varied across populations and traits. Overall, our results for multiple, elite maize populations indicated that targeted recombination is a most promising breeding approach.